Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future. In practice, emergency applications often require less training time. However, there is a little work on how to obtain good prediction performance with less training time. In this paper, we propose a simplified deep residual network for our problem. By using the simplified deep residual network, we can obtain not only less training time but also competitive prediction performance compared with the existing similar method. Moreover, we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost. Based on the real datasets, we construct a series of experiments compared with the existing methods. The experimental results confirm the efficiency of our proposed methods.

人群流量预测是城市计算的一个重要问题，其目标是预测未来区域内传入和传出的人数。在实践中，紧急应用通常需要较少的培训时间。但是，关于如何在较少的培训时间下获得良好的预测性能的工作还很少。在本文中，我们针对问题提出了简化的深度残差网络。与现有的类似方法相比，通过使用简化的深度残差网络，我们不仅可以获得更少的训练时间，而且还具有竞争性的预测性能。此外，我们采用时空注意机制以合理的额外时间成本进一步改进简化的深度残差网络。在真实数据集的基础上，我们与现有方法进行了一系列实验。